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Top 10 Real-Life Examples Of Machine Learning

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Machine learning is a subdivision of artificial intelligence where machines learn from the data, identify patterns and take decisions. In ML, machines are trained to work independently without being programmed or with minimal human intervention.

Organizations across various industries utilize ML technologies to make business decisions, improve productivity, diagnose disease, forecast weather, and much more. Some of the machine learning uses are mentioned below:

  1. Image Recognition

It's one of the widespread uses of machine learning. A machine distinguishes an object as a digital image based on the pixel's intensity in black & white and colored images. For example, in a black and white image, each pixel's intensity is served as one of the measurements, while in a colored image, each pixel provides three measurements of intensities in three colors – red, green, and blue (RGB). Some of the examples of image recognition are

  • Label an x-ray as cancerous or not
  • Tagging on social media
  • Handwriting recognition
  1. Traffic Alerts

You must have seen Google Maps suggesting you the fastest route or traffic on the road. Machine learning helps to avoid traffic and reach your destination on time. It collects data and gives results based on multiple factors such as the number of people using Google Maps, previous data on that route, and real-time data such as your average traveling speed, location, etc.  

  1. Speech Recognition

Speech recognition is interpreting spoken words into text. It is also called automatic speech recognition or computer speech recognition. Specific software applications recognize the words in audio and then convert the audio into a text file. You can also segment the speech signal by the intensity in different time-frequency bands too. Some of its typical applications are

  • Voice search
  • Voice dialing
  • Appliance control
  1. Medical Diagnosis

Machine learning offers tools to help with the diagnosis and prognosis of the disease. In rare diseases, facial recognition helps scan patient photos and identify phenotypes associated with rare genetic disorders. Many practitioners use chatbots with speech recognition to distinguish patterns in symptoms.

Examples of medical diagnosis

  • Assist in formulating a diagnosis or recommends a treatment option
  • Recognize cancerous tissue
  • Analyze bodily fluids
  1. Statistical Arbitrage

In finance, arbitrage is a short-term automated strategy that helps manage volumes of securities. In this strategy, a trading algorithm is applied to analyze a set of securities based on economic variables and correlations.

Examples of statistical arbitrage:

  • Analyze a market microstructure
  • Identifying real-time arbitrage opportunities
  • Analyzing large data sets
  • Optimizing arbitrage strategy to enhance results.
  1. Predictive Analytics

It is one of the promising examples of machine learning and is applicable on everything ranging from real estate pricing to product development. Machine learning classifies the available data into groups and then they are defined by set rules by the analysts. After completing the classification, the probability of the fault is calculated.

Examples of predictive analytics:

  • Predicting if a transaction is legitimate or fraudulent
  • Improve prediction systems to estimate the possibility of fault
  1. Product Recommendation

You must have received recommendations while shopping on eCommerce brands like Amazon and Flipkart, such as 'users who bought it also bought'; 'users bought this along with this product!

All these are the result of machine learning algorithms that learn from users' patterns and recommend new or additional products to buy based on that data.

  1. Extraction

It's probably one of the best machine learning applications where structured information is extracted from unstructured data. A good machine learning bootcamp teaches you the best practice for data extraction. The source of information such as the web pages, articles, blogs, reports, and emails are the input that outputs the structured data.

  1. Real-Time Dynamic Pricing

You must have seen the flight ticket prices or your uber fare rise during the peak hours. This is due to high demand. Machine learning techniques are used for dynamic pricing considering weather, competition, demand, occasion, local issues, etc. This data also helps suggest discounted prices, best prices, promotions, etc.

  1. Regression

In regression, the principle of machine learning is used to optimize the parameters. It is used to reduce the approximation error and derive the closest possible outcome. Machine learning can also be used for function optimization. You can choose to change the inputs to get the closest possible outcome.  

So, these were some of the popular applications of machine learning. If machine learning as a career intrigues you, it's time to look for a reputed machine learning training.  It will help you learn various types of machine learning, such as supervised, unsupervised, and semi-supervised machine learning.

Also, Read This Blog: What is Machine Learning? A Definition